[HTML][HTML] Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation
Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
for the creation and optimization of electrocatalysts, which enhance key electrochemical …
Automation and machine learning augmented by large language models in a catalysis study
Y Su, X Wang, Y Ye, Y Xie, Y Xu, Y Jiang, C Wang - Chemical Science, 2024 - pubs.rsc.org
Recent advancements in artificial intelligence and automation are transforming catalyst
discovery and design from traditional trial-and-error manual mode into intelligent, high …
discovery and design from traditional trial-and-error manual mode into intelligent, high …
Latent Variable Machine Learning Framework for Catalysis: General Models, Transfer Learning, and Interpretability
GO Kayode, MM Montemore - JACS Au, 2023 - ACS Publications
Machine learning has been successfully applied in recent years to screen materials for a
variety of applications. However, despite recent advances, most screening-based machine …
variety of applications. However, despite recent advances, most screening-based machine …
Computational Design of Catalysts with Experimental Validation: Recent Successes, Effective Strategies, and Pitfalls
H Hosseini, CJ Herring, CF Nwaokorie… - The Journal of …, 2024 - ACS Publications
Computation has long proven useful in understanding heterogeneous catalysts and
rationalizing experimental findings. However, computational design with experimental …
rationalizing experimental findings. However, computational design with experimental …
Bayesian-optimization-based design of highly active and stable Fe–Cu/SSZ-13 catalysts for the selective catalytic reduction of NO x with NH 3
Catalysts for the selective catalytic reduction of nitrogen oxides (NOx) with NH3 are currently
limited by low activity at low temperatures and deactivation under hydrothermal conditions …
limited by low activity at low temperatures and deactivation under hydrothermal conditions …
[HTML][HTML] Accelerating high-entropy alloy discovery: efficient exploration via active learning
The exploration of the complex composition space for high entropy alloys (HEAs) is
extremely challenging and resource intensive using traditional materials discovery …
extremely challenging and resource intensive using traditional materials discovery …
cAIMD Simulations Guided Design of Atomic Praseodymium Doping In–Bi Nanofibers for High‐Energy‐Efficiency CO2 Electrolysis to Formate in Ultra‐Wide Potential …
Y Li, Y Jin, X Zhang, M Fu, R Lin, G Li… - Advanced Functional … - Wiley Online Library
The electrochemical CO2 reduction reaction (ECO2RR) has emerged as a promising
technology for achieving carbon neutralization. Even though considerable efforts are …
technology for achieving carbon neutralization. Even though considerable efforts are …
Machine Learning Predictions of Metal Alloy Properties
GA Sulley - 2024 - search.proquest.com
Metal alloys, including bimetallic alloys and high entropy alloys (HEAs), possess distinctive
properties driving diverse applications across catalysis, additive manufacturing, aerospace …
properties driving diverse applications across catalysis, additive manufacturing, aerospace …